Statistics in the Law: A Practitioner's Guide, Cases, and Materials

Hardcover | June 6, 2008

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The book will serve primarily as a user's manual or desk reference for the expert witness-lawyer team and secondarily as a textbook or supplemental textbook for upper level undergraduate statistics students. It starts with two articles by masters of the trade, Paul Meier and Franklin Fisher.It then explains the distinction between the Frye and Daughbert standards for expert testimony, and how these standards play out in court. The bulk of the book is concerned with individual cases ranging over a wide variety of topics, such as electronic draw poker (does it require skill to play),employment discrimination (how to tell whether an employer discriminated against older workers in deciding whom to fire), driving while black (did the New Jersey State Police disproportionately stop blacks), jury representativeness (is a jury a representative cross section of the community), jurieshearing death penalty cases (are such juries biased toward a guilty verdict, and does the Supreme Court care), the civil incarceration of violent sexual offenders after having served their jail sentences (can future dangerousness be predicted), do data from multiple choice examinations support anallegation of copying, whether rental agents in an apartment complex steered African-American prospects to one part of the complex, how much tax is owed after an audit that used a random sample, whether an inventor falsified his notebook in an effort to fool the Patent Office, and whether ballotshad been tampered with in an election. The book concludes with two recent English cases, one in which a woman was accused of murdering her infant sons because both died of \cot death" or \sudden death syndrome," (she was convicted, but later exonerated), and how Bayesian analyses can (or moreprecisely), cannot be presented in UK courts. In each study, the statistical analysis is shaped to address the relevant legal questions, and draws on whatever methods in statistics might shed light on those questions.

Joseph B. Kadane is a Leonard J. Savage University Professor of Statistics and Social Sciences, Emeritus at Carnegie Mellon University